Embedded sensing systems conventionally perform A-D conversion followed by signal processing to apply specific analyses on sensor data. In many applications, the analysis of interest is inference (e.g., classification). The challenge is that, increasingly, the sensor signals are too complex to model analytically. Machine-learning algorithms are gaining prominence since they overcome the need to model signals analytically, instead enabling data-driven methods of training a classifier. Prior classification systems employ a basic architecture that receives an analog signal for classification. The signal is amplified via an instrumentation amplifier. The amplified signal is fed into an analog to digital converter (ADC). The ADC output is then subjected to a series of digital multiply and accumulate operations (MAC). The result of these amplification, ADC and MAC operations is a classification output. What is needed is a new hardware architecture that enables direct data conversion of information during the ADC process.